Semantic search is a sophisticated search technique that goes beyond traditional keyword matching to capture the intent, context, and meaning behind a user's query. It utilizes AI models to interpret semantic relationships between words and find contextually relevant results, making it incredibly useful in areas like knowledge management, recommendation systems, information retrieval, AI agents, and more. In this guide, we'll show you how to implement semantic search in your PostgreSQL database using Cohere's embedding models, simplifying the workflow with pgvector and PopSQL. We'll demonstrate a high-level overview of the steps involved, including data preparation, vectorizer setup, indexing, and query execution. By leveraging vector embeddings and AI-powered search techniques, we can deliver highly relevant results that focus on the meaning behind text rather than exact keyword matches, revolutionizing information retrieval in various applications.